{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#蓍草卜卦 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大衍之数五十，其用四十有九。分而为二以象两，挂一以象三，揲之以四以象四时，归奇于扐以象闰。五岁再闰，故再扐而后挂。天一，地二；天三，地四；天五，地六；天七，地八；天九，地十。天数五，地数五。五位相得而各有合，天数二十有五，地数三十，凡天地之数五十有五，此所以成变化而行鬼神也。乾之策二百一十有六，坤之策百四十有四，凡三百六十，当期之日。二篇之策，万有一千五百二十，当万物之数也。是故四营而成《易》，十有八变而成卦，八卦而小成。引而伸之，触类而长之，天下之能事毕矣。显道神德行，是故可与酬酢，可与祐神矣。子曰：“知变化之道者，其知神之所为乎。”\n",
    "\n",
    "大衍之数五十，存一不用，构造天地人三者，历经三变，第一次的余数是5或9，第二次的是4或8，第三次的是4或8，剩下的数量除以4就是结果。即为一爻，算六爻要一个小时。古人构造随机数的方法太费时间啦。用Python写个程序来搞吧！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def sepSkyEarth(data):\n",
    "    sky =  random.randint(1, data-2)  \n",
    "    earth = data - sky\n",
    "    earth -= 1\n",
    "    return sky , earth\n",
    "\n",
    "\n",
    "def getRemainder(num):\n",
    "    rm = num % 4\n",
    "    if rm == 0:\n",
    "        rm = 4\n",
    "    return rm\n",
    "\n",
    "def getChange(data):\n",
    "    sky, earth = sepSkyEarth(data)\n",
    "    skyRemainder = getRemainder(sky)\n",
    "    earthRemainder = getRemainder(earth)\n",
    "    change = skyRemainder + earthRemainder + 1\n",
    "    data = data - change\n",
    "    return sky, earth, change, data\n",
    "\n",
    "def getYao(data):\n",
    "    sky, earth, firstChange, data = getChange(data)\n",
    "    sky, earth, secondChange, data = getChange(data)\n",
    "    sky, earth, thirdChange, data = getChange(data)\n",
    "    yao  = data/4\n",
    "    return yao, firstChange, secondChange, thirdChange\n",
    "\n",
    "def sixYao():\n",
    "    yao1 = getYao(data = 50 - 1)[0]\n",
    "    yao2 = getYao(data = 50 - 1)[0]\n",
    "    yao3 = getYao(data = 50 - 1)[0]\n",
    "    yao4 = getYao(data = 50 - 1)[0]\n",
    "    yao5 = getYao(data = 50 - 1)[0]\n",
    "    yao6 = getYao(data = 50 - 1)[0]\n",
    "    return[yao1, yao2, yao3, yao4, yao5, yao6]\n",
    "\n",
    "def fixYao(num):\n",
    "    if num == 6 or num == 9:\n",
    "        print \"there is a changing predict! Also run changePredict()\"\n",
    "    return num % 2\n",
    "    \n",
    "\n",
    "def changeYao(num):\n",
    "    if num == 6:\n",
    "        num = 1\n",
    "    elif num == 9:\n",
    "        num = 2\n",
    "    num = num % 2\n",
    "    return(num)\n",
    "\n",
    "def fixPredict(pred):\n",
    "    fixprd = [fixYao(i) for i in pred]\n",
    "    fixprd = list2str(fixprd)\n",
    "    return fixprd\n",
    "\n",
    "def list2str(l):\n",
    "    si = ''\n",
    "    for i in l:\n",
    "        si = si +  str(i)\n",
    "    return si\n",
    "\n",
    "def changePredict(pred):\n",
    "    changeprd = [changeYao(i) for i in pred]\n",
    "    changeprd = list2str(changeprd)\n",
    "    return changeprd\n",
    "\n",
    "def getPredict():\n",
    "    pred = sixYao()\n",
    "    fixPred = fixPredict(pred)\n",
    "    if 6 in pred or 9 in pred:\n",
    "        changePred = changePredict(pred)\n",
    "    else:\n",
    "        changePred = None\n",
    "    return fixPred, changePred  \n",
    "\n",
    "def interpretPredict(now, future):\n",
    "    dt = {'111111':'乾','011111':'夬','000000':'坤','010001':'屯','100010':'蒙','010111':'需','111010':'讼','000010':'师',\n",
    "'010000':'比','110111':'小畜','111011':'履','000111':'泰','111000':'否','111101':'同人','101111':'大有','000100':'谦',\n",
    "'001000':'豫','011001':'随','100110':'蛊','000011':'临','110000':'观','101001':'噬嗑','100101':'贲','100000':'剥',\n",
    "'000001':'复','111001':'无妄','100111':'大畜','100001':'颐','011110':'大过','010010':'坎','101101':'离','011100':'咸',\n",
    "'001110':'恒','111100':'遁','001111':'大壮','101000':'晋','000101':'明夷','110101':'家人','101011':'睽','010100':'蹇',\n",
    "'001010':'解','100011':'损','110001':'益','111110':'姤','011000':'萃','000110':'升','011010':'困','010110':'井',\n",
    "'011101':'革','101110':'鼎','001001':'震','100100':'艮','110100':'渐','001011':'归妹','001101':'丰','101100':'旅',\n",
    "'110110':'巽','011011':'兑','110010':'涣','010011':'节','110011':'中孚','001100':'小过','010101':'既济','101010':'未济'}\n",
    "    if future:\n",
    "        name = dt[now] + ' & ' + dt[future]\n",
    "    else:\n",
    "        name = dt[now]\n",
    "    print name\n",
    " \n",
    "\n",
    "def plotTransitionRemainder(N, w):\n",
    "    import matplotlib.cm as cm\n",
    "    import matplotlib.pyplot as plt\n",
    "    from collections import defaultdict\n",
    "    \n",
    "    changes = {}\n",
    "    for i in range(N):\n",
    "        sky, earth, firstChange, data = getChange(data = 50 -1)\n",
    "        sky, earth, secondChange, data = getChange(data)\n",
    "        sky, earth, thirdChange, data = getChange(data)\n",
    "        changes[i]=[firstChange, secondChange, thirdChange, data/4]\n",
    "\n",
    "    ichanges = changes.values()\n",
    "\n",
    "    firstTransition = defaultdict(int)\n",
    "    for i in ichanges:\n",
    "        firstTransition[i[0], i[1]]+=1\n",
    "\n",
    "    secondTransition = defaultdict(int)\n",
    "    for i in ichanges:\n",
    "        secondTransition[i[1], i[2]]+=1\n",
    "\n",
    "    thirdTransition = defaultdict(int)\n",
    "    for i in ichanges:\n",
    "        thirdTransition[i[2], i[3]]+=1    \n",
    "        \n",
    "    cmap = cm.get_cmap('Accent_r', len(ichanges))\n",
    "\n",
    "    for k, v in firstTransition.iteritems(): \n",
    "        plt.plot([1, 2], k, linewidth = v*w/N)\n",
    "    for k, v in secondTransition.iteritems(): \n",
    "        plt.plot([2, 3], k, linewidth = v*w/N)\n",
    "    for k, v in thirdTransition.iteritems(): \n",
    "        plt.plot([3, 4], k, linewidth = v*w/N)\n",
    "    plt.xlabel(u'Time')\n",
    "    plt.ylabel(u'Changes')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#大衍之数五十，存一不用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = 50 - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###一变"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32 \n",
      "16 \n",
      "9 \n",
      "40\n"
     ]
    }
   ],
   "source": [
    "sky, earth, firstChange, data = getChange(data)\n",
    "print sky, '\\n', earth, '\\n',firstChange, '\\n', data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###二变"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 \n",
      "24 \n",
      "8 \n",
      "32\n"
     ]
    }
   ],
   "source": [
    "sky, earth, secondChange, data = getChange(data)\n",
    "print sky, '\\n', earth, '\\n',secondChange, '\\n', data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###三变"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 \n",
      "21 \n",
      "4 \n",
      "28\n"
     ]
    }
   ],
   "source": [
    "sky, earth, thirdChange, data = getChange(data)\n",
    "print sky, '\\n', earth, '\\n',thirdChange, '\\n', data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#得到六爻及变卦"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there is a changing predict! Also run changePredict()\n",
      "there is a changing predict! Also run changePredict()\n",
      "there is a changing predict! Also run changePredict()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('110101', '000001')"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getPredict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there is a changing predict! Also run changePredict()\n",
      "there is a changing predict! Also run changePredict()\n",
      "there is a changing predict! Also run changePredict()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('011110', '110100')"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getPredict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('101100', None)"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "getPredict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#得到卦名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there is a changing predict! Also run changePredict()\n",
      "屯 & 比\n"
     ]
    }
   ],
   "source": [
    "fixPred, changePred   = getPredict()\n",
    "interpretPredict(fixPred, changePred  )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#添加卦辞"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "#http://baike.fututa.com/zhouyi64gua/\n",
    "\n",
    "import urllib2\n",
    "from bs4 import BeautifulSoup\n",
    "import os\n",
    "\n",
    "# set work directory\n",
    "os.chdir('/Users/chengjun/github/iching/')\n",
    "\n",
    "dt = {'111111':'乾','011111':'夬','000000':'坤','010001':'屯','100010':'蒙','010111':'需','111010':'讼','000010':'师',\n",
    "    '010000':'比','110111':'小畜','111011':'履','000111':'泰','111000':'否','111101':'同人','10111':'大有','000100':'谦',\n",
    "    '001000':'豫','011001':'随','100110':'蛊','000011':'临','110000':'观','101001':'噬嗑','100101':'贲','100000':'剥',\n",
    "    '000001':'复','111001':'无妄','100111':'大畜','100001':'颐','011110':'大过','010010':'坎','101101':'离','011100':'咸',\n",
    "    '001110':'恒','111100':'遁','001111':'大壮','101000':'晋','000101':'明夷','110101':'家人','101011':'睽','010100':'蹇',\n",
    "    '001010':'解','100011':'损','110001':'益','111110':'姤','011000':'萃','000110':'升','011010':'困','010110':'井',\n",
    "    '011101':'革','101110':'鼎','001001':'震','100100':'艮','110100':'渐','001011':'归妹','001101':'丰','101100':'旅',\n",
    "    '110110':'巽','011011':'兑','110010':'涣','010011':'节','110011':'中孚','001100':'小过','010101':'既济','101010':'未济'}\n",
    "\n",
    "\n",
    "dr = {}\n",
    "for i, j in dt.iteritems():\n",
    "    dr[unicode(j, 'utf8')]= i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "url = \"http://baike.fututa.com/zhouyi64gua/\"\n",
    "content = urllib2.urlopen(url).read() #获取网页的html文本\n",
    "soup = BeautifulSoup(content) \n",
    "articles = soup.find_all('div', {'class', 'gualist'})[0].find_all('a')\n",
    "links = [i['href'] for i in articles]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'http://baike.fututa.com/a5738', u'http://baike.fututa.com/a5739']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "links[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://baike.fututa.com/a5738 1\n",
      "http://baike.fututa.com/a5739 2\n",
      "http://baike.fututa.com/a5740 3\n",
      "http://baike.fututa.com/a5741 4\n",
      "http://baike.fututa.com/a5742 5\n",
      "http://baike.fututa.com/a5743 6\n",
      "http://baike.fututa.com/a5744 7\n",
      "http://baike.fututa.com/a5745 8\n",
      "http://baike.fututa.com/a5746 9\n",
      "http://baike.fututa.com/a5747 10\n",
      "http://baike.fututa.com/a5748 11\n",
      "http://baike.fututa.com/a5749 12\n",
      "http://baike.fututa.com/a5750 13\n",
      "http://baike.fututa.com/a5751 14\n",
      "http://baike.fututa.com/a5752 15\n",
      "http://baike.fututa.com/a5753 16\n",
      "http://baike.fututa.com/a5754 17\n",
      "http://baike.fututa.com/a5755 18\n",
      "http://baike.fututa.com/a5756 19\n",
      "http://baike.fututa.com/a5757 20\n",
      "http://baike.fututa.com/a5758 21\n",
      "http://baike.fututa.com/a5759 22\n",
      "http://baike.fututa.com/a5760 23\n",
      "http://baike.fututa.com/a5761 24\n",
      "http://baike.fututa.com/a5762 25\n",
      "http://baike.fututa.com/a5763 26\n",
      "http://baike.fututa.com/a5764 27\n",
      "http://baike.fututa.com/a5765 28\n",
      "http://baike.fututa.com/a5766 29\n",
      "http://baike.fututa.com/a5767 30\n",
      "http://baike.fututa.com/a5768 31\n",
      "http://baike.fututa.com/a5769 32\n",
      "http://baike.fututa.com/a5770 33\n",
      "http://baike.fututa.com/a5771 34\n",
      "http://baike.fututa.com/a5772 35\n",
      "http://baike.fututa.com/a5773 36\n",
      "http://baike.fututa.com/a5774 37\n",
      "http://baike.fututa.com/a5775 38\n",
      "http://baike.fututa.com/a5776 39\n",
      "http://baike.fututa.com/a5777 40\n",
      "http://baike.fututa.com/a5778 41\n",
      "http://baike.fututa.com/a5779 42\n",
      "http://baike.fututa.com/a5780 43\n",
      "http://baike.fututa.com/a5781 44\n",
      "http://baike.fututa.com/a5782 45\n",
      "http://baike.fututa.com/a5783 46\n",
      "http://baike.fututa.com/a5784 47\n",
      "http://baike.fututa.com/a5785 48\n",
      "http://baike.fututa.com/a5786 49\n",
      "http://baike.fututa.com/a5787 50\n",
      "http://baike.fututa.com/a5788 51\n",
      "http://baike.fututa.com/a5789 52\n",
      "http://baike.fututa.com/a5790 53\n",
      "http://baike.fututa.com/a5791 54\n",
      "http://baike.fututa.com/a5792 55\n",
      "http://baike.fututa.com/a5793 56\n",
      "http://baike.fututa.com/a5794 57\n",
      "http://baike.fututa.com/a5795 58\n",
      "http://baike.fututa.com/a5796 59\n",
      "http://baike.fututa.com/a5797 60\n",
      "http://baike.fututa.com/a5798 61\n",
      "http://baike.fututa.com/a5799 62\n",
      "http://baike.fututa.com/a5800 63\n",
      "http://baike.fututa.com/a5801 64\n"
     ]
    }
   ],
   "source": [
    "dtext = {}\n",
    "\n",
    "from time import sleep\n",
    "\n",
    "num = 0\n",
    "for j in links:\n",
    "    sleep(0.1)\n",
    "    num += 1\n",
    "    ghtml = urllib2.urlopen(j).read() #获取网页的html文本\n",
    "    print j, num\n",
    "    gua = BeautifulSoup(ghtml, from_encoding = 'gb18030') \n",
    "    guaName =  gua.title.text.split('_')[1].split(u'卦')[0]\n",
    "    guaId = dr[guaName]\n",
    "    guawen = gua.find_all('div', {'class', 'gua_wen'})\n",
    "    guaText = []\n",
    "    for i in guawen:\n",
    "        guaText.append(i.get_text() + '\\n\\n')\n",
    "    guaText = ''.join(guaText)\n",
    "    dtext[guaId] = guaText"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dtextu = {}\n",
    "for i, j in dtext.iteritems():\n",
    "    dtextu[i]= j.encode('utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       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      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtext.values()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import json\n",
    "with open(\"/Users/chengjun/github/iching/package_data.dat\",'w') as outfile:\n",
    "    json.dump(dtextu, outfile, ensure_ascii=False) #, encoding = 'utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dat = json.load(open('package_data.dat'), encoding='utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "观卦原文观。盥而不荐，有孚顒若。象曰：风行地上，观。先王以省方，观民设教。白话文解释观卦：祭祀时灌酒降神而不献人牲，因为用作祭祀的俘虏的头部肿了，不能用作祭品。《象辞》说：本卦上卦为巽为风，下卦为坤为地，风行大地吹拂万物，这是观的卦象。先王观此卦象取法于周流八方的风，从而巡视邦国，观察民情，推行教化。\n",
      "\n",
      "《断易天机》解观卦巽上坤下，为乾宫四世卦。观卦象征将道义展示于众人面前。观卦风在上而地在下，是春风浩荡、万物滋生之象。\n",
      "\n",
      "北宋易学家邵雍解以下观上，周游观览；平心静气，坚守岗位。得此卦者，处身于变化之中，心神不宁，宜多观察入微，待机行事，切勿妄进。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：出外游览，不易闲居。财运：贩卖洋货，须防风险。家宅：供养神佛。身体：风湿之症，运动调养。\n",
      "\n",
      "传统解卦这个卦是异卦（下坤上巽）相叠，风行地上，喻德教遍施。观卦与临卦互为综卦，交相使用。在上者以道义观天下；在下者以敬仰瞻上，人心顺服归从。大象：风运行于坤地之上，喻有周游观览之意。阴长阳消，正道衰微，万物难行。运势：处身于变化之中，宜多观察入微，心身苦恼，并防外来诱惑。事业：事业已出现不顺利的迹象，务必谦虚慎重，高瞻远瞩，尤忌短期行为，心胸开阔，注意搞好人际关系，团结他人，不宜轻率行动。必要时不妨投靠德行高的人，以图再度发展。经商：市场形势不稳定，正处于变动，应谨慎小心，随时观察动向，从长计议，做好退却的准备。求名：贵在自知之明，虚心向他人求教，端正处世态度，尤其不可自我满足，更须守中持正，树立远大理想，勿丧失个人追求的目标，勿偏离正道。婚恋：不顺利，双方应经受住考验，从长计议，会有理想的结果。决策：具有组织能力，适宜担任负责工作，但人生旅途多曲折，必须注意培养自己的预见性和观察力，办事务必公正，实事求是，注意人心动向，关心他人。尤其不可以陷入小人之道和近视行为之中。应有决断精神，不可盲从，必要时可进行策略依附。\n",
      "\n",
      "台湾张铭仁解卦观：表示观察、观看，是个最标准的状况卦，吉凶未定。凡事不可轻下决定，需要再观察一下局势再做打算。尤其是对长官、上位者的态度更是要察言观色，不可轻忽大意。解释：尚在观察中。特性：有创意，喜欢开发新事物，见解新颖，爱旅行，观摩他人，研究比较，不断修正反省自己。运势：处在有危险不稳的时运，耍多观望时势之利弊，待机行事，于人事物，须以诚待之，才能感化而幸免陷入困难也。家运：正处于下坡时段，要知固守进退之机，听取长辈意见有助也。疾病：有渐恶化之象，病情变化多，为腹、神经系统病症。胎孕：孕有不安之象。子女：儿女将来有成就幸福也。周转：虽有好言，实际内在空虚。无指望居多。买卖：交易有碍。应在戌亥日可成，否则难成。等人：观望必有等待，而来者有随他人去之象。寻人：此人到处漂泊，行踪不定，有险象。目前在西南方，宜丑未日见。失物：若能细心四察，可以找回来。外出：有风险，择日而行。考试：不理想。诉讼：小人当道，愈争论惹起纷争愈多，但终会和解。求事：不易。改行：擅自主张不宜，若由他人提拔则可。开业：开业者不宜。\n",
      "\n",
      "初六爻辞初六。童观，小人无咎，君子吝。象曰：初六童观，小人道也。白话文解释初六：愚蠢近视，对一般百姓来讲尚无大碍，但对于担负政治责任的君子来说，将会铸成大错。《象辞》说：初六爻辞讲，愚蠢近视，这正是小人们的思想特征。\n",
      "\n",
      "北宋易学家邵雍解凶：得此爻者，宜防小人，诸事有困难，弄巧成拙。做官的有危难，不宜进取。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：初运未佳，但无大碍。财运：初登场面，小作即可。家宅：小心童仆；自由结亲。身体：小孩没事，大人不利。\n",
      "\n",
      "初六变卦初六爻动变得周易第42卦：风雷益。这个卦是异卦（下震上巽）相叠。巽为风；震为雷。风雷激荡，其势愈强，雷愈响，风雷相助互长，交相助益。此卦与损卦相反。它是损上以益下，后者是损下以益上。二卦阐述的是损益的原则。\n",
      "\n",
      "六二爻辞六二。窥观，利女贞。象曰：窥观女贞，亦可丑也。白话文解释六二：囿于一孔之见，这是有利于女人的贞兆。《象辞》说：女人窥视男人，即使操行贞正，亦属可丑的行为。\n",
      "\n",
      "北宋易学家邵雍解平：得此爻者，在家不如意，外出谋事则好，或喜或忧，或因妇人而起是非，大抵宜动不宜静。此爻女喜，男悲。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：最好退守，女性有利。财运：蚕丝有利，余皆不宜。家宅：妇女主家。身体：阴寒之症，可愈。\n",
      "\n",
      "六二变卦六二爻动变得周易第59卦：风水涣。这个卦是异卦（下坎上巽）相叠。风在水上行，推波助澜，四方流溢。涣，水流流散之意。象征组织和人心涣散，必须用积极的手段和方法克服，战胜弊端，挽救涣散，转危为安。\n",
      "\n",
      "六三爻辞六三。观我生，进退。象曰：观我生进退，未失道也。白话文解释六三：观察亲族的思想动向，从而决定为政的措施。《象辞》说：观察亲族的思想动向，从而决定为政的措施，这才未失去用人行政的正道。\n",
      "\n",
      "北宋易学家邵雍解平：得此爻者，三心二意，须谨慎行事，知难而退。做官的进退无常。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：度德量力，稍安毋躁。财运：随买随卖，不致有失。家宅：守住旧债。身体：安心静养，可保平安。\n",
      "\n",
      "六三变卦六三爻动变得周易第53卦：风山渐。这个卦是异卦（下艮上巽）相叠。艮为山，巽为木。山上有木，逐渐成长，山也随着增高。这是逐渐进步的过程，所以称渐，渐即进，渐渐前进而不急速。\n",
      "\n",
      "六四爻辞六四。观国之光，利用宾于王。象曰：观国之光，尚宾也。白话文解释六四：观察国家政绩风俗的辉煌表现。筮遇此爻，有利于朝觐君王。《象辞》说：观察国家政绩风俗的辉煌表现，此来者为国宾。\n",
      "\n",
      "北宋易学家邵雍解平：得此爻者，宜出外谋事，多与外人交往，则有利。做官的或居闲职，清高之地。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：正当好运，名胜于利。财运：国际贸易，有利有名。家宅：喜事临门。身体：多加小心。\n",
      "\n",
      "六四变卦六四爻动变得周易第12卦：天地否。这个卦是异卦（下坤上乾）相叠，其结构同泰卦相反，系阳气上升，阴气下降，天地不交，万物不通。它们彼此为“综卦”，表明泰极而否，否极泰来，互为因果。\n",
      "\n",
      "九五爻辞九五。观我生，君子无咎。象曰：观我生，观民也。白话文解释九五：善于观察亲族之意向，君子可以无过错。《象辞》说：观察亲族之意向，就是观察天下万民的意向。\n",
      "\n",
      "北宋易学家邵雍解吉：得此爻者，时来运转，利益丰厚，对于妇人则有怀孕之喜，病重者有生命之忧。做官的会因为自己良好的官德，爵禄有升。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：直道而行，无往不利。财运：我来决定，必可得利。家宅：我来建宅。身体：平安无碍。\n",
      "\n",
      "九五变卦九五爻动变得周易第23卦：山地剥。这个卦是异卦（下坤上艮）相叠。五阴在下，一阳在上，阴盛而阳孤；高山附于地。二者都是剥落象，故为“剥卦”。此卦阴盛阳衰，喻小人得势，君子困顿，事业败坏。\n",
      "\n",
      "上九爻辞上九。观其生，君子无咎。象曰：观其生，志未平也。白话文解释上九：观察其他部族的意向，君子可以无过错。《象辞》说：观察其他部族的意向，是因为尚未全面摸清情况，心不踏实，决心难下。\n",
      "\n",
      "北宋易学家邵雍解平：得此爻者，运势不通，心有余而力不足，怀孕者利于生育，久病者会痊愈。做官的宜退守，修德养身。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：大运已过，自省无碍。财运：买到货存，有利可图。家宅：老宅生息。身体：来日无多。\n",
      "\n",
      "上九变卦上九爻动变得周易第8卦：水地比。这个卦是异卦（下坤上坎）相叠，坤为地，坎为水。水附大地，地纳河海，相互依赖，亲密无间。此卦与师卦完全相反，互为综卦。它阐述的是相亲相辅，宽宏无私，精诚团结的道理。\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print dat.values()[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there is a changing predict! Also run changePredict()\n"
     ]
    }
   ],
   "source": [
    "now, future = getPredict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def ichingText(k):\n",
    "    import json\n",
    "    dat = json.load(open('iching/package_data.dat'))\n",
    "    print dat[k]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "既济卦原文既济。亨，小利贞，初吉终乱。象曰：水在火上，既济。君子以思患而预防之。白话文解释既济卦：亨通。这是小见吉利的贞卜。起初吉利，最后将发生变故。《象辞》说：本卦上卦为坎，坎为水；下卦为离，离为火。水上火下，水浇火熄，是既济之卦的卦象。君子观此卦象，从而有备于无患之时，防范于未然之际。\n",
      "\n",
      "《断易天机》解既济卦坎上离下，为坎宫三世卦。既济就是说已经成功，事情已经摆平。但还需谨慎，当心物极必反。\n",
      "\n",
      "北宋易学家邵雍解济助有成，坚忍自重；由大而小，确保盛运。得此卦者，事业有成，成功之象，但谨防盛极必衰，宜退守为吉，再进则凶。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：盛极必衰，谨防后患。财运：及时出手，可得利润。家宅：大厦可居；百年好合。身体：大病初愈，仍需自理。\n",
      "\n",
      "传统解卦这个卦是异卦（下离上坎）相叠。坎为水，离为火，水火相交，水在火上，水势压倒火势，救火大功告成。既，已经；济，成也。既济就是事情已经成功，但终将发生变故。大象：坎水在离火之上，水性下注，火势上炎，水火相济，完成之意。运势：名利双收，成功之象，但须防物极必反，初吉后凶。事业：处在顺利和兴旺的阶段，甚至大功已经告成。如何才能保住这种势头，是问题的关键。必须认识盛极将衰的真理，思患预防，防微杜渐，有备可无患。同时，一定要密切注意形势，抓住有利时机，继续奋发向上，推动事业发展。经商：由于市场形势有利，已经赚得许多，但同时也预示着危机的随时出现。因此，务必小心在意，事未到，先预防，终日提防戒惧。同时，认真观察市场的变化，随时转向，开拓新市场。求名：个人追求名利的基本条件已经具备。必须防止因此而变得僵化和保守，以致功亏一篑，半途而废。婚恋：起初顺利，中途会出现挫折。决策：各方面条件都很好，环境又有利于成长。但因此反而会使自己满足，停止前进，甚至变得颓废，趋向没落。一定要不断刻苦努力，保持谦虚美德，常怀不安之心，防止不测事件发生。\n",
      "\n",
      "台湾张铭仁解卦既济：表示水火阴阳调和，一时平安和乐之象。水火本相克，因一时环境或人事所影响，而暂时相安无事，但终非常久之象，必有所冲克而败。解释：已经得到利益，不可贪得无厌。特性：物质丰盛，但精神易空虚，外华内虚，交际广，知心少，心思细腻，多愁善感，喜宁静的生活。运势：功名双收。极盛之时，但须知物极必反，宜退守为吉，再进则凶。但得此卦者，皆有因一时顺利而忘本，大意失荆州，终有人不和、心迷乱之象。家运：生在富家之象，受祖上恩泽而不知珍惜者多，终而破运之兆。疾病：短期虽有好转，但好景不常。胎孕：不安。子女：初时幸福，长大后与双亲不和，慎之。周转：短期可成，长期无望。买卖：中途变卦不利。故初时见好就收，莫贪心拖延，否则不利。等人：已中途折回，不必再等。寻人：若是常出走之人，小时自回，长大再出走，难寻。甚或不回。失物：应可寻回。外出：利于出行，途中需注意安全。考试：实力有，但需视考期之日吉凶而定，小吉。诉讼：有理说不清，仍须把握最有利时机争取主动，迟则反见凶。求事：紧急可，不宜长久。改行：不宜。开业：开业者最好慎重​​考虑，因此卦象好景不常。\n",
      "\n",
      "初九爻辞初九。曳其轮，濡其尾，无咎。象曰：曳其轮，义无咎也。白话文解释初九：提着腰带过河，打湿了衣尾。没有大问题。《象辞》说：提着腰带过河，打湿了衣尾，理应无灾难。\n",
      "\n",
      "北宋易学家邵雍解平：得此爻者，时运未到，欲动未动，安保无虞。做官的有职未受，有位未登。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：奋勉有加，可以无咎。财运：暂时稳住，将可脱困。家宅：可以安居；初聘为吉。身体：初病可治。\n",
      "\n",
      "初九变卦初九爻动变得周易第39卦：水山蹇。这个卦是异卦（下艮上坎）相叠。坎为水，艮为山。山高水深，困难重重，人生险阻，见险而止，明哲保身，可谓智慧。蹇，跋行艰难。\n",
      "\n",
      "六二爻辞六二。妇丧其茀，勿逐，七日得。象曰：七日得，以中道也。白话文解释六二：妇人丢失了头巾，不用寻找，七日内可以不寻而得。《象辞》说：七日内可以不寻而得，因为六二阴爻居阴位，位象既得，失物将还。\n",
      "\n",
      "北宋易学家邵雍解平：得此爻者，先难后易，做官的有先逆后顺之美。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：不计小利，得成大功。财运：失而复得，不必担心。家宅：七年可还；将可团聚。身体：七日可愈。\n",
      "\n",
      "六二变卦六二爻动变得周易第5卦：水天需。这个卦是异卦（下乾上坎）相叠，下卦是乾，刚健之意；上卦是坎，险陷之意。以刚逢险，宜稳健之妥，不可冒失行动，观时待变，所往一定成功。\n",
      "\n",
      "九三爻辞九三。高宗伐鬼方，三年克之。小人勿用。象曰：三年克之，惫也。白话文解释九三：高宗讨伐鬼方，费时三年才打败它。筮遇此爻，不可重用小人。《象辞》说：费时三年才打败它，因为鬼方这时已疲惫不堪。\n",
      "\n",
      "北宋易学家邵雍解凶：得此爻者，有结怨争诉之损。做官的多差遣征伐之举。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：努力工作，所谋必成。财运：数年经营，才可获利。家宅：三年后居；三年可婚。身体：一时无恙，只保三年。\n",
      "\n",
      "九三变卦九三爻动变得周易第3卦：水雷屯。这个卦是异卦（下震上坎）相叠，震为雷，喻动；坎为雨，喻险。雷雨交加，险象丛生，环境恶劣。“屯”原指植物萌生大地，万物始生，充满艰难险阻，然而顺时应运，必欣欣向荣。\n",
      "\n",
      "六四爻辞六四。繻有衣袽，终日戒。象曰：终日戒，有所疑也。白话文解释六四：撑着用败絮塞罅漏的船，整日里提心吊胆。《象辞》说：整日里提心吊胆，说明心中疑虑重重。\n",
      "\n",
      "北宋易学家邵雍解平：得此爻者，时运平平，有备则无患。做官的须做好预防，则爵禄稳固。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：处顺思困，谨慎为宜。财运：经营漏洞，早些修补。家宅：修正屋宇；珍惜情谊。身体：年老体弱。\n",
      "\n",
      "六四变卦六四爻动变得周易第49卦：泽火革。这个卦是异卦（下离上兑）相叠。离为火、兑为泽，泽内有水。水在上而下浇，火在下而上升。火旺水干，水大火熄。二者相生亦相克，必然出现变革。变革是宇宙的基本规律。\n",
      "\n",
      "九五爻辞九五。东邻杀牛，不如西郊之禴祭，实受其福。象曰：东邻杀牛，不如西邻之时也。实受其福，吉大来也。白话文解释九五：殷人杀牛厚祭鬼神，不如周人簿祭鬼神，周人倒是得到鬼神的福佑。《象辞》说：殷人杀牛厚祭鬼神，不如周人薄祭鬼神的用意美善，周人得到鬼神的福佑，将有重大的吉庆降临。\n",
      "\n",
      "北宋易学家邵雍解吉：得此爻者，近谋有实，远谋多虚，不利东方，利于西方。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：持盈保泰，吉凶自取。财运：奢不如俭，实获其利。家宅：向西吉宅；两邻订亲。身体：最好祷告。\n",
      "\n",
      "九五变卦九五爻动变得周易第36卦：地火明夷。这个卦是异卦（下离上坤）相叠。离为明，坤为顺；离为日；坤为地。日没入地，光明受损，前途不明，环境困难，宜遵时养晦，坚守正道，外愚内慧，韬光养晦。\n",
      "\n",
      "上六爻辞上六。濡其首，厉。象曰：濡其首厉，何可久也。白话文解释上六：涉水过河，水拍湿其头部，危险。《象辞》说：水拍湿其头部，怎能呆很久的时间呢？\n",
      "\n",
      "北宋易学家邵雍解凶：得此爻者，小人侵扰，坐船者，防溺水之灾。做官的有过刚则折之忧。\n",
      "\n",
      "台湾国学大儒傅佩荣解时运：好运已过，小心危险。财运：首次买卖，必难获利。家宅：不利长房；元配有灾。身体：头部有疾，危险可知。\n",
      "\n",
      "上六变卦上六爻动变得周易第37卦：风火家人。这个卦是异卦（下离上巽）相叠。离为火；巽为风。火使热气上升，成为风。一切事物皆应以内在为本，然后伸延到外。发生于内，形成于外。喻先治家而后治天下，家道正，天下安乐。\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "ichingText(future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0xgxyffAQTOksWQKnnWZzIZOz18MNTfzJf3sfWJj4xsnLgzlz4rGQSTb49FM4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\nC76iKEpM+P9vNyIJrciNXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ce4fb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plotTransitionRemainder(10000, w = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dpupMb9yxWCxMnDiRlStXkpCQwPLlnzFs2KvExLxNWtoWmjW7n4iIGr7/6oip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CAAAg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\nDZxInbOnc6UcKHvO/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NVHZ2WvnNFkQt/sbKZrxvrYWCQ3e9gmRm3tGZSUrMKZMx+itrb1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\nmSMiIiIiIjKh/wcsAZzsrJhUPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c7c34d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(15, 10),facecolor='white')\n",
    "plt.subplot(2, 2, 1)\n",
    "plotTransitionRemainder(1000, w = 50)\n",
    "plt.subplot(2, 2, 2)\n",
    "plotTransitionRemainder(1000, w = 50)\n",
    "plt.subplot(2, 2, 3)\n",
    "plotTransitionRemainder(1000, w = 50)\n",
    "plt.subplot(2, 2, 4)\n",
    "plotTransitionRemainder(1000, w = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dt = {'111111':u'乾','011111':u'夬','000000':u'坤','010001':u'屯','100010':u'蒙','010111':u'需','111010':u'讼','000010':'师',\n",
    "'010000':u'比','110111':u'小畜','111011':u'履','000111':u'泰','111000':u'否','111101':u'同人','101111':u'大有','000100':u'谦',\n",
    "'001000':u'豫','011001':u'随','100110':u'蛊','000011':u'临','110000':u'观','101001':u'噬嗑','100101':u'贲','100000':'u剥',\n",
    "'000001':u'复','111001':u'无妄','100111':u'大畜','100001':u'颐','011110':u'大过','010010':u'坎','101101':u'离','011100':u'咸',\n",
    "'001110':u'恒','111100':u'遁','001111':u'大壮','101000':u'晋','000101':u'明夷','110101':u'家人','101011':u'睽','010100':u'蹇',\n",
    "'001010':u'解','100011':u'损','110001':u'益','111110':u'姤','011000':u'萃','000110':u'升','011010':u'困','010110':u'井',\n",
    "'011101':u'革','101110':u'鼎','001001':u'震','100100':u'艮','110100':u'渐','001011':u'归妹','001101':u'丰','101100':u'旅',\n",
    "'110110':u'巽','011011':u'兑','110010':u'涣','010011':u'节','110011':u'中孚','001100':u'小过','010101':u'既济','101010':u'未济'\n",
    "      }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "观\n",
      "益\n",
      "家人\n",
      "渐\n",
      "蹇\n",
      "既济\n",
      "小过\n",
      "丰\n",
      "大过\n",
      "夬\n",
      "震\n",
      "豫\n",
      "兑\n",
      "困\n",
      "升\n",
      "泰\n",
      "临\n",
      "师\n",
      "艮\n",
      "贲\n",
      "遁\n",
      "同人\n",
      "蒙\n",
      "损\n",
      "鼎\n",
      "大有\n",
      "无妄\n",
      "否\n",
      "睽\n",
      "未济\n",
      "中孚\n",
      "涣\n",
      "坎\n",
      "节\n",
      "需\n",
      "井\n",
      "巽\n",
      "小畜\n",
      "萃\n",
      "随\n",
      "大壮\n",
      "恒\n",
      "革\n",
      "咸\n",
      "解\n",
      "归妹\n",
      "离\n",
      "坤\n",
      "复\n",
      "大畜\n",
      "蛊\n",
      "明夷\n",
      "谦\n",
      "乾\n",
      "姤\n",
      "颐\n",
      "u剥\n",
      "屯\n",
      "比\n",
      "旅\n",
      "讼\n",
      "履\n",
      "晋\n",
      "噬嗑\n"
     ]
    }
   ],
   "source": [
    "for i in dt.values():\n",
    "    print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dtu = {}\n",
    "for i, j in dt.iteritems():\n",
    "    dtu[i] = unicode(j, 'utf-8')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'110000': u'\\u89c2', '110001': u'\\u76ca', '110101': u'\\u5bb6\\u4eba', '110100': u'\\u6e10', '010100': u'\\u8e47', '010101': u'\\u65e2\\u6d4e', '001100': u'\\u5c0f\\u8fc7', '001101': u'\\u4e30', '011110': u'\\u5927\\u8fc7', '011111': u'\\u592c', '001001': u'\\u9707', '001000': u'\\u8c6b', '011011': u'\\u5151', '011010': u'\\u56f0', '000110': u'\\u5347', '000111': u'\\u6cf0', '000011': u'\\u4e34', '000010': u'\\u5e08', '100100': u'\\u826e', '100101': u'\\u8d32', '111100': u'\\u9041', '111101': u'\\u540c\\u4eba', '100010': u'\\u8499', '100011': u'\\u635f', '101110': u'\\u9f0e', '101111': u'\\u5927\\u6709', '111001': u'\\u65e0\\u5984', '111000': u'\\u5426', '101011': u'\\u777d', '101010': u'\\u672a\\u6d4e', '110011': u'\\u4e2d\\u5b5a', '110010': u'\\u6da3', '010010': u'\\u574e', '010011': u'\\u8282', '010111': u'\\u9700', '010110': u'\\u4e95', '110110': u'\\u5dfd', '110111': u'\\u5c0f\\u755c', '011000': u'\\u8403', '011001': u'\\u968f', '001111': u'\\u5927\\u58ee', '001110': u'\\u6052', '011101': u'\\u9769', '011100': u'\\u54b8', '001010': u'\\u89e3', '001011': u'\\u5f52\\u59b9', '000000': u'\\u5764', '101101': u'\\u79bb', '000001': u'\\u590d', '100111': u'\\u5927\\u755c', '100110': u'\\u86ca', '000101': u'\\u660e\\u5937', '000100': u'\\u8c26', '111111': u'\\u4e7e', '111110': u'\\u59e4', '100001': u'\\u9890', '100000': u'\\u5265', '010001': u'\\u5c6f', '010000': u'\\u6bd4', '101100': u'\\u65c5', '111010': u'\\u8bbc', '111011': u'\\u5c65', '101000': u'\\u664b', '101001': u'\\u566c\\u55d1'}\n"
     ]
    }
   ],
   "source": [
    "def ichingDate(d):\n",
    "    import random\n",
    "    random.seed(d)\n",
    "    try:\n",
    "        print 'Your birthday & your prediction time:', str(d)\n",
    "    except:\n",
    "        print('Your birthday & your prediction time:', str(d))\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
